Provisioning dynamic machine learning (ML) inference as a service for artificial intelligence (AI) applications of edge devices faces many challenges, including the trade-off among accuracy loss, carbon emission, and unknown future costs. Besides, many governments are launching carbon emission rights (CER) for operators to reduce carbon emissions further to reverse climate change. Facing these challenges, to achieve carbon-aware ML task offloading under limited carbon emission rights thus to achieve green edge AI, we establish a joint ML task offloading and CER purchasing problem, intending to minimize the accuracy loss under the long-term time-averaged cost budget of purchasing the required CER. However, considering the uncertainty of the resource prices, the CER purchasing prices, the carbon intensity of sites, and ML tasks' arrivals, it is hard to decide the optimal policy online over a long-running period time. To overcome this difficulty, we leverage the two-timescale Lyapunov optimization technique, of which the $T$-slot drift-plus-penalty methodology inspires us to propose an online algorithm that purchases CER in multiple timescales (on-preserved in carbon future market and on-demanded in the carbon spot market) and makes decisions about where to offload ML tasks. Considering the NP-hardness of the $T$-slot problems, we further propose the resource-restricted randomized dependent rounding algorithm to help to gain the near-optimal solution with no help of any future information. Our theoretical analysis and extensive simulation results driven by the real carbon intensity trace show the superior performance of the proposed algorithms.

翻译：为边缘设备的人工智能（AI）应用提供动态机器学习（ML）推理服务面临诸多挑战，包括精度损失、碳排放与未知未来成本之间的权衡。此外，许多政府正为运营商发放碳排放权（CER），以进一步减少碳排放并应对气候变化。面对这些挑战，为在有限的碳排放权下实现碳感知的ML任务卸载，从而达到绿色边缘AI，我们建立了一个联合ML任务卸载与CER购买问题，旨在最小化长期时间平均成本预算（用于购买所需CER）下的精度损失。然而，考虑到资源价格、CER购买价格、站点碳强度以及ML任务到达的不确定性，很难在长时间运行周期内在线确定最优策略。为克服这一困难，我们利用双时间尺度Lyapunov优化技术，其中$T$时隙漂移加惩罚方法启发我们提出一种在线算法，该算法在多个时间尺度上购买CER（在碳期货市场上预先保留，在碳现货市场上按需购买），并做出ML任务卸载决策。考虑到$T$时隙问题的NP-hard性质，我们进一步提出资源限制随机化依赖舍入算法，无需任何未来信息即可获得近优解。基于真实碳强度轨迹的理论分析与广泛仿真结果展示了所提算法的优越性能。